Uncertainty Quantification (UQ) is an emerging and extremely active research discipline
which aims to quantitatively treat any uncertainty in applied models. The primary objective of …

Deterministic finite-dimensional variational inequality problems represent a tool for capturing
a broad range of optimization and equilibrium problems in application settings ranging from …

In the paper we study, in a Hilbert space setting, a general random traffic equilibrium
problem and characterize the random Wardrop equilibrium distribution by means of a …

Variational inequality problems allow for capturing an expansive class of problems,
including convex optimization problems, convex Nash games and economic equilibrium …

In this note, we investigate stochastic Nash equilibrium problems by means of monotone
variational inequalities in probabilistic Lebesgue spaces. We apply our approach to a class …

The aim of the paper is to introduce a random elastic traffic equilibrium problem in a Hilbert
space setting. The equilibrium condition is expressed by a random extension of the elastic …

Sample average approximation (SAA) approach for two-stage stochastic variational
inequalities (SVIs) with continuous probability distributions, where the second-stage …

This paper aims to study stochastic variational inequalities with special emphasis to
incorporate uncertainty in transportation models. The main contribution of this paper is two …

In this paper, we consider a variational inequality model of migration in which populations
move according to the utility functions corresponding to various locations and movement …

In this paper, we study the competition of healthcare institutions for medical supplies in
emergencies caused by natural disasters. In particular, we develop a two-stage procurement …